Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource (HR) attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform [1]. Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees' satisfaction level as compared to other attributes.
As the early diagnosis of autism spectrum disorder (ASD) is critical the high accuracy machine learning can be applied to achieve technology based diagnosis. In this context, the present study demonstrates machine learning approach for ASD diagnosis using decision tree (DT) modeling. The dataset employed in the present study comprises two classes of ASD adults with a sample size of 704 instances. The DT model entails a recursive partitioning approach implemented in the “rpart” package of R. The optimum model is derived by tuning parameters such as Min split, Min bucket, Max depth, and complexity. The performance of the model is evaluated in terms of the mean square error estimate of the error rate.
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